.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gallery/extras/plot_coupling_heatmap.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_gallery_extras_plot_coupling_heatmap.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_gallery_extras_plot_coupling_heatmap.py:


Coupling heatmap of a sphere
=================================

PyMieSim makes it easy to create a source and a scatterer. With these objects
defined, it is possible to use PyMieSim to find the scattering efficiency of the
scatterer. This feature can be used to plot a graph of the scattering efficiency
of a sphere as a function of the permittivity and the size parameter.

.. GENERATED FROM PYTHON SOURCE LINES 10-67



.. image-sg:: /gallery/extras/images/sphx_glr_plot_coupling_heatmap_001.png
   :alt: Coupling power [Watt]
   :srcset: /gallery/extras/images/sphx_glr_plot_coupling_heatmap_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import numpy

    from PyMieSim.experiment.detector import Photodiode
    from PyMieSim.experiment.scatterer import Sphere
    from PyMieSim.experiment.source import Gaussian
    from PyMieSim.experiment import Setup
    from PyMieSim.units import degree, watt, AU, nanometer, RIU

    import matplotlib.pyplot as plt


    index = numpy.linspace(1.3, 2.1, 100) * RIU

    diameter = numpy.linspace(1, 2000, 100) * nanometer

    source = Gaussian(
        wavelength=400 * nanometer,
        polarization=90 * degree,
        optical_power=1e-3 * watt,
        NA=0.2 * AU
    )


    scatterer = Sphere(
        diameter=diameter,
        property=index,
        medium_property=1 * RIU,
        source=source
    )


    detector = Photodiode(
        polarization_filter=0 * degree,
        NA=0.3 * RIU,
        phi_offset=0 * degree,
        gamma_offset=0 * degree,
        sampling=400 * AU
    )

    experiment = Setup(scatterer=scatterer, source=source, detector=detector)

    dataframe = experiment.get('coupling', add_units=False)

    values = dataframe.values.reshape([diameter.size, index.size])

    figure, ax = plt.subplots(1, 1)

    image = ax.pcolormesh(index, diameter * 1e6, values, shading='auto')
    ax.set(
        xlabel="Permittivity",
        ylabel=r'Diameter [$\mu$m]',
        title="Coupling power [Watt]"
    )
    plt.colorbar(mappable=image)

    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 2.274 seconds)


.. _sphx_glr_download_gallery_extras_plot_coupling_heatmap.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_coupling_heatmap.ipynb <plot_coupling_heatmap.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_coupling_heatmap.py <plot_coupling_heatmap.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_coupling_heatmap.zip <plot_coupling_heatmap.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_